2 citations · 9 across the 7 of their papers we have counts for
7 papers
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
Zhaozhe Hu, Jia-Li Yin, Bin Chen +3
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous resear…
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization
Jia-Li Yin, Haoyuan Zheng, Ximeng Liu
Robust Unsupervised Domain Adaptation (RoUDA) aims to achieve not only clean but also robust cross-domain knowledge transfer from a labeled source domain to an unlabeled target dom…
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability
Bin Chen, Jia-Li Yin, Shukai Chen +2
While the transferability property of adversarial examples allows the adversary to perform black-box attacks (i.e., the attacker has no knowledge about the target model), the trans…
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data
Zekai Chen, Fuyi Wang, Zhiwei Zheng +2
Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, adversaries can manipul…
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
Wanqing Zhu, Jia-Li Yin, Bo-Hao Chen +1
As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target datase…
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness
Jia-Li Yin, Lehui Xie, Wanqing Zhu +2
In response to the threat of adversarial examples, adversarial training provides an attractive option for enhancing the model robustness by training models on online-augmented adve…
Robust Single-step Adversarial Training with Regularizer
Lehui Xie, Yaopeng Wang, Jia-Li Yin +1
High cost of training time caused by multi-step adversarial example generation is a major challenge in adversarial training. Previous methods try to reduce the computational burden…